With the rapid development of technology and the prevailing of social platforms, people tend to provide various kinds of information (e.g., text messages, voice messages) by a variety of input interfaces in their daily life. How to correctly interpret information provided by people so as to provide appropriate responses and/or services based on the interpreted information is an increasingly significant issue in different application fields (e.g., human machine interfaces).
Conventional Chinese semantic analysis technologies may be divided into two categories. While one category utilizes a deep learning network to determine the intention indicated by a Chinese character string, the other category utilizes a keyword analysis technology to label keywords in a Chinese character string. No matter which category of technology is employed, word segmentation needs to be performed on the Chinese character string before the Chinese semantic analysis is performed.
Correctness of conventional Chinese semantic analysis technologies heavily relies on the correctness of the word segmentation. Although many word segmentation technologies are available currently, they have difficulties in handling issues of “ambiguity identification” and “new word identification” when performing word segmentation on a Chinese character string. “Ambiguity identification” means that a Chinese character sting may have more than two word segmentation results. “New word identification” means that the Chinese character string includes words and phrases unregistered, i.e., words and phrases which are not recorded in dictionaries but are actually used. Since the conventional word segmentation technologies cannot deal with the two issues, the result of subsequent Chinese semantic analysis becomes not accurate enough.
Consequently, a semantic analysis technology that reduces the dependence on the word segmentation technology and improves the correctness of semantic analysis is in an urgent need.